Vibration-based structural health monitoring

University of Technology Sydney. Faculty of Engineering and Information Technology.Ageing and degradation of the infrastructures, especially critical infrastructures (such as power plants, high-rise buildings, long-span bridges, dams, airports, railway tracks, etc.) impose a major concern that affects national assets of each country and endangers public safety. On this point, structural health monitoring (SHM) potentially provides solution to the problem by evaluating the integrity of the infrastructures to determine their current health state. Basically, structural health monitoring deals with early warning on the state of health of infrastructures, localisation and quantification of damage in the structures and prediction of their remaining service life. This, consequently reduces asset management costs, effectively prolongs operational lifetime and ensures public safety. Hence, getting access to a robust paradigm to deal with aforementioned concerns is a major challenge introduced here. Despite high level of research activities in this field, few robust methods of indicating an adverse condition of a structure in service has been demonstrated as effective, which is the motivation for this research work. The main objective of this study is to investigate a more robust scheme of damage identification, including damage detection and damage localisation, to overcome some of the shortcomings with the current methods. In this regard, firstly, a background on the existing methods is presented in Chapter 1 to evaluate the advantages and limitations with the current methods. According to the literature, frequency response-based damage identification methods are superior to conventional modal-based approaches as they are less error prone and provide abundance of information in a wide range of frequencies; hence, this study starts with developing a more robust frequency response function (FRF) – based damage identification method in Chapter 2 to detect and localise structural damage in single or multiple states. The novelty of the approach is implementation of two-dimensional discrete wavelet transform (2-D DWT) along with the second derivative of the reconstructed operational mode shapes obtained by FRFs to enhance the sensitivity of the approach to damage. Based on the numerical results of this stage, it can be concluded that the method’s performance is quite acceptable once the level of undesirable noise is negligible; however, by increasing the level of uncertainty in the system, the performance of the method deteriorates. Therefore, to overcome this problem, the harmful effects of measurement noise on FRFs are investigated in Chapter 3 and its undesirable effect is suppressed by employing a novel idea using Gaussian Kernel on FRFs. The numerical and experimental damage identification results obtained by the presented noise-suppression approach demonstrate its efficiency to cope with the issue of noise. At this stage, and although, the method is performing well, in terms of its sensitivity to damage as well as its robustness against noise, it still needs to be modified; in real-life applications the source of excitation is random and this important issue has not been taken into account in the methodology described in Chapter 3. Hence, in Chapter 4, the issue of stochastic systems is investigated and a novel spectral-based approach is presented to deal with the issue of random excitation in damage identification process. In this regard, the frequency distribution of the power spectral density of the time responses is analysed by introducing the spectral moments which represent some major statistical properties of a stochastic process. The efficiency of the approach is validated by several numerical case studies. The method presented in Chapter 2 to Chapter 4 is a frequency-based approach and, therefore, raw time measured responses are first required for transfer from time domain into the frequency domain before further analysis. In some applications, it might be advantageous to deal with directly measured time responses without transferring them into the frequency domain. In this regard, in Chapter 5, a novel time series-based damage identification method is presented based on the idea of symbolic time series analysis. The main idea of the method is to generate the symbol sequences by mapping the time data from the state space into the constructed symbol space and then study any change in the statistical properties of the obtained symbol sequences by developing the probability vectors. This method is very easy to implement, is robust against noise and it has shown a considerable sensitivity to slight structural damage. The efficiency of the method is successfully demonstrated by several numerical and experimental investigations. The scope of Chapter 1 to Chapter 5 of this thesis is mainly on structural damage identification; however, it might be of great interest to obtain a reliable and representative model of the structure considering the effects of structural damage. This is of considerable demand, as in many applications, it is required to estimate the serviceability and remaining life of a structure after damage occurrence. Because of this, the author explores the issue of finite element model updating in Chapter 6 to investigate how a reliable model of the structure can be obtained. A particular damage case is investigated and the idea of thin layer interface elements is introduced and implemented. It is demonstrated, that by updating the finite element model of the structure, using this technique, the reliability of the model significantly improves. According to this journey from Chapter 1 to Chapter 6, more robust schemes of damage identification are developed and verified both in time and frequency domains; and, some future works are suggested by the author in Chapter 7 to conclude the work and motivate other researchers to pursue the work further

Automated Operational Modal Analysis of a Cable-Stayed Bridge

© 2017 American Society of Civil Engineers. Automated techniques for analyzing the dynamic behavior of full-scale civil structures are becoming increasingly important for continuous structural health-monitoring applications. This paper describes an experimental study aimed at the identification of modal parameters of a full-scale cable-stayed bridge from the collected output-only vibration data without the need for any user interactions. The work focuses on the development of an automated and robust operational modal analysis (OMA) algorithm, using a multistage clustering approach. The main contribution of the work is to discuss a comprehensive case study to demonstrate the reliability of a novel criterion aimed at defining the hierarchical clustering threshold to enable the accurate identification of a complete set of modal parameters. The proposed algorithm is demonstrated to work with any parametric system identification algorithm that uses the system order n as the sole parameter. In particular, the results from the covariance-driven stochastic subspace identification (SSI-Cov) methods are presented

Guided-Wave-Based Damage Detection in Steel Pipes

© 2020, Springer Nature Singapore Pte Ltd. Pipelines are important infrastructural components for petro-chemical transportation. Millions of kilometres of pipelines are used to transport oil, natural gas and water. Pipeline leakage is a critical issue caused by corrosion or deterioration. This may yield to catastrophic failure or even life-threatening consequences. To prevent this, reliable and effective condition assessment should be employed to the routine maintenance. Non-destructive evaluation is the predominant method in the field of defect inspection. Guided wave testing is extensively used because of its low energy attenuation and long inspection distance. In this study, application of ultrasonic guided waves for damage detection in thin-wall steel pipes is investigated to better understand the wave propagation behaviour in presence of various damage scenarios in steel pipes. Guided waves have drawn increasing attentions in the research community of structural health monitoring (SHM) owning to their capability of identifying minor damages, however, the presence of boundaries in the structure and wave reflections produce challenges in signal processing to extract damage-related information from the disturbed signals. In this research, the commercial finite element (FE) analysis software ANSYS is used to simulate the wave propagation phenomenon in steel pipes. Longitudinal wave mode is excited on the top surface of pipe by applying external deformation. The reflection with damage information is clearly captured by ‘pitch-catch’ configuration. Multiple damage scenarios are introduced in the steel pipes by reducing the pipe thickness at different locations and with different severities. The transient response analysis is conducted to extract the dynamic wave responses in the structure followed by two wavelet-based damage indices to detect, assess and localise damage. Our extensive investigations demonstrate that the proposed method has potential for detection, assessment and localization of damage in steel pipes with a limited number of sensors

FRF-based damage localization method with noise suppression approach

© 2014 Elsevier Ltd. In this paper a noise-robust damage identification method is presented for localization of structural damage in presence of heavy noise influences. The method works based on Frequency Response Functions (FRFs) of the damaged structure without any prior knowledge of the healthy state. The main innovation of this study starts with convolving FRFs with Gaussian kernel to suppress the noise. Denoised signals are then used to develop shape signals according to the second derivative of the operational mode shapes at frequencies in the half-power bandwidth of the center resonant frequencies. The scheme is followed by normalization of shape signals to create a two-dimensional map indicating the damage pattern. The validation of the method was carried out based on simulated data and experimental measurements. The simulated data polluted with 10 percent random noise considering four different conditions: (i) un-correlated noise with Gaussian distribution (ii) noise with non-Gaussian exponential distribution (iii) noise with non-Gaussian Log-normal distribution and (iv) correlated colored noise. The robustness of the method was examined with respect to the damage severity with various damage conditions. Finally, damage detection experiments of a fixed-fixed steel beam are presented to illustrate the feasibility and effectiveness of the proposed method. According to the numerical and experimental investigations, it was demonstrated that the proposed approach presents satisfactory damage indices both in single and multiple damage states in presence of high level noise. Hence, the method can overcome the problems of output measurement noise and deliver encouraging results on damage localization

Application of symbolic time series analysis for damage localisation in truss structures

Reliability of truss bridges can be significantly affected by local damages as damage changes the load path in the structure. As damage increases, the load-carrying capacity of the structure considerably reduces which might result in catastrophic failure. Hence, it is important to detect structural damages as early stage as possible to avoid further propagation. In the present work, a time series-based method is proposed to detect and localise damage in truss structures. The method works based on Symbolic Time Series Analysis (STSA) of time responses to localise a gradually evolving deterioration in the structure according to the changes in the statistical behaviour of symbol sequences. First, the symbol sequences are generated by transforming the measured time data to symbol space to reduce the dimension of information and then the probability vectors for each symbol sequence is created. Damage localisation is carried out by comparing the probability vectors of different measured locations. It is expected that the damaged member shows a higher degree of variation in the probability vector which is introduced as damage sensitive feature. Numerical demonstrations on a plane truss are presented to illustrate the accuracy and efficiency of the proposed method. Gradually evolving damage is introduced by the stiffness reduction in truss members. Finite element technique is employed to obtain the time response of the structure subjected to ambient vibration. The simulated responses are polluted with random noise to take into account the influence of practical uncertainties. Simulation results under various damage conditions demonstrate the effectiveness of the proposed algorithm in detection and localisation of gradually evolving damage in single or multiple states in presence of measurement noise up to 5%

A FRF-based damage detection method utilising wavelet decomposition

Damage in a structure causes deviation in dynamic responses of the structure either in frequency or time domain in comparison with its healthy status. The purpose of this study is to present a new damage detection method in order to detect and localize the structural damage. This novel algorithm is based on the directly-measured frequency response functions (FRFs). The approach is composed of three major steps: first, developing the curvature of FRFs which produces spatially distributed shape functions at each frequency coordinate, secondly, normalization of FRFs' curvature in order to enhance the influence of the lower-frequency-band data; finally decomposition of the obtained profiles (normalized version of FRFs' curvature) by conducting wavelet analysis to detect any possible structural abnormality through structure. The combination of these three steps leads to a robust algorithm in detection and localisation of any damage in the structure even at small levels which other FRF-based methods were unable to detect. There are some benefits with the presented method: first, this method does not need higher-frequency-range data which is hard to obtain in most civil applications; second, there is no need for baseline data from the intact structure; This is particularly attractive for practical applications as it opens an opportunity for online monitoring of the structural integrity without demanding for any previous data records of the structure. The performance of the method is evaluated on a numerical model and the effect of different parameters such as the location of the excitation point, the level and the location of the damage was studied; the results demonstrated that the method can efficiently identify the location of the damage in the structure even for damage at small levels. © 2013 Taylor & Francis Group

A comparative study on the performance of the damage detection methods in the frequency domain

During last two decades, a vast number of damage detection methods have been proposed either in frequency or time domain. These methods normally have their own advantages and limitations or suitable applications; the purpose of this study is to examine the performance of the some popular methods on localisation a possible damage on a sample structure. All of the chosen methods are based on the frequency domain data and work based on proposing a damage sensitive indicator which contains spatial information. Mode shape curvature, frequency response functions' curvature, modal strain energy, flexibility matrix and spatial wavelet transform were amongst those damage detection methods were chosen for this study. The case study considers a clamped-clamped beam which was modelled by solid elements in order to define several damage stages based on different crack depth. Damage was simulated by reduction in elastic modulus of the elements in damage zone. The transient response of the structure due to an external impact excitation was obtained by ANSYS and then polluted by different percentages of white noise. The time-domain responses at selected evenly-spaced locationswas then processed byMATLAB to achieve the FRFs and mode shapes respectively by applying Fourier transform and eigenvalue realization algorithm (ERA). Based on the obtained results, it was found that despite some of these methods were suggested by so many researchers, they completely fail in localising damage in the structure even at high level of damage severity. © 2013 Taylor & Francis Group

A tensor-based structural damage identification and severity assessment

Early damage detection is critical for a large set of global ageing infrastructure. Structural Health Monitoring systems provide a sensor-based quantitative and objective approach to continuously monitor these structures, as opposed to traditional engineering visual inspection. Analysing these sensed data is one of the major Structural Health Monitoring (SHM) challenges. This paper presents a novel algorithm to detect and assess damage in structures such as bridges. This method applies tensor analysis for data fusion and feature extraction, and further uses one-class support vector machine on this feature to detect anomalies, i.e., structural damage. To evaluate this approach, we collected acceleration data from a sensor-based SHM system, which we deployed on a real bridge and on a laboratory specimen. The results show that our tensor method outperforms a state-of-the-art approach using the wavelet energy spectrum of the measured data. In the specimen case, our approach succeeded in detecting 92.5% of induced damage cases, as opposed to 61.1% for the wavelet-based approach. While our method was applied to bridges, its algorithm and computation can be used on other structures or sensor-data analysis problems, which involve large series of correlated data from multiple sensors

Nothing-on-Road Axle Detection Strategies in Bridge-Weigh-in-Motion for a Cable-Stayed Bridge: Case Study

© 2018 American Society of Civil Engineers. This case-study article aims to share the field-test observations of a real-world cable-stayed bridge with the research community of bridge-weigh-in-motion to address the challenges of axle identification. Various structural members of the bridge, including cables, girders, and the deck, were instrumented with strain gauges at different locations to measure the axial, bending, or shear strain responses. Numerous field tests were conducted by running light and heavy vehicles traveling at different speeds, in different traffic directions, and in different lateral locations on the bridge. Because the identification of closely spaced axles is important to ensuring true classification of the vehicles, vehicles with tandem- and tridem-axle configurations were adopted in the field test. The study aimed to identify the sensor arrangement through which the closely spaced axles can be reliably detected regardless of the speed, traveling direction, and lateral location of the vehicle on the bridge. It was found that the axial strains on the cables and bending strains in the girders provided the global response of the structure; hence, they were unable to identify the closely spaced axles. In contrast, it was observed that the longitudinal strains under the deck were able to identify the closely spaced axles, provided they were positioned as closely as possible to the wheel path. Finally, the shear responses at the end of the span were able to identify the closely spaced axles irrespective of the traveling direction and lateral location of the vehicle. In this study, due to the testing limitations, including the short span of the bridge and the presence of a roundabout at one end of the bridge, it was not feasible to maintain a constant speed; therefore, identification of axle weight and axle spacing, which requires a constant-speed assumption, is not discussed

Frequency domain decomposition-based multisensor data fusion for assessment of progressive damage in structures

© 2018 John Wiley & Sons, Ltd. In this paper, we focused on the development and verification of a solid and robust framework for structural condition assessment of real-life structures using measured vibration responses, with the presence of multiple progressive damages occurring within the inspected structures. A self-tuning learning method for structural condition assessment was proposed. Damage sensitive features were extracted using a frequency domain decomposition (FDD) approach to fuse all the measured responses, followed by random projection algorithm for dimensionality reduction. An automatic parameter selection method called Appropriate Distance to the Enclosing Surface (ADES) was used for tuning the classifier parameter. The effect of operational conditions on the robustness of the proposed method was also investigated, and it was realized that application of FDD to extract damage sensitive feature reduces the variation in the results. Promising results in the assessment of damage were obtained based on two comprehensive case studies, which included single and multiple damage scenarios. The contributions of the work are threefold. First, through two comprehensive case studies, we demonstrate that the frequency-based feature from a single sensor might not be adequate enough to detect the progress of damage, even if the sensor is in the vicinity of damage. Second, we show that data fusion using FDD can reliably assess the severity of damage, and finally, we propose a new automated approach for tuning the classifier parameter